Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model

Abstract

Autonomous vehicles (AVs) can generate major changes in urban systems due to their ability to use road infrastructures more efficiently and shorten trip times. However, there is great uncertainty about these effects and about whether the use of these vehicles will continue to be private, in continuity with the current paradigm, or whether they will become shared (carsharing/ridesharing). In order to try to shed light on these matters, the use of a scenario-based methodology and the evaluation of the scenarios using a land use–transport interaction model (LUTI model TRANSPACE) is proposed. This model allows simulating the impacts that changes in the transport system can generate on the location of households and companies oriented to local demand and accessibility conditions. The obtained results allow us to state that, if AVs would generate a significant increase in the capacity of urban and interurban road infrastructures, the impacts on mobility and on the location of activities could be positive, with a decrease in the distances traveled, trip times, and no evidence of significant urban sprawl processes. However, if these increases in capacity are accompanied by a large augment in the demand for shared journeys by new users (young, elderly) or empty journeys, the positive effects could disappear. Thus, this scenario would imply an increase in trip times, reduced accessibilities, and longer average distances traveled, all of which could cause the unwanted effect of expelling activities from the consolidated urban center.

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sustainability
Article
Modeling the Impacts of Autonomous Vehicles on Land Use
Using a LUTI Model
Rubén Cordera * , Soledad Nogués , Esther González-González and JoséLuis Moura


Citation: Cordera, R.; Nogués, S.;
González-González, E.; Moura, J.L.
Modeling the Impacts of
Autonomous Vehicles on Land Use
Using a LUTI Model. Sustainability
2021,13, 1608. https://doi.org/
10.3390/su13041608
Academic Editor: Pan Lu
Received: 31 December 2020
Accepted: 30 January 2021
Published: 3 February 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Transport and Projects and Processes Technology, School of Civil Engineering, University of
Cantabria, Av. de Los Castros 44, 39005 Santander, Cantabria, Spain; soledad.nogues@unican.es (S.N.);
mariaesther.gonzalez@unican.es (E.G.-G.); joseluis.moura@unican.es (J.L.M.)
*Correspondence: ruben.cordera@unican.es; Tel.: +34-942-206-741
Abstract:
Autonomous vehicles (AVs) can generate major changes in urban systems due to their
ability to use road infrastructures more efficiently and shorten trip times. However, there is great un-
certainty about these effects and about whether the use of these vehicles will continue to be private, in
continuity with the current paradigm, or whether they will become shared (carsharing/ridesharing).
In order to try to shed light on these matters, the use of a scenario-based methodology and the
evaluation of the scenarios using a land use–transport interaction model (LUTI model TRANSPACE)
is proposed. This model allows simulating the impacts that changes in the transport system can
generate on the location of households and companies oriented to local demand and accessibility
conditions. The obtained results allow us to state that, if AVs would generate a significant increase in
the capacity of urban and interurban road infrastructures, the impacts on mobility and on the location
of activities could be positive, with a decrease in the distances traveled, trip times, and no evidence
of significant urban sprawl processes. However, if these increases in capacity are accompanied
by a large augment in the demand for shared journeys by new users (young, elderly) or empty
journeys, the positive effects could disappear. Thus, this scenario would imply an increase in trip
times, reduced accessibilities, and longer average distances traveled, all of which could cause the
unwanted effect of expelling activities from the consolidated urban center.
Keywords: autonomous vehicles; land use; LUTI model; accessibility
1. Introduction
The introduction of autonomous vehicles (AVs) in cities has generated great expec-
tations and, at the same time, considerable concern about future mobility patterns and
the potential changes they may represent to urban forms. In a context where society is
more open to new forms of mobility, it is expected that AVs will favor a more efficient,
safe, inclusive, and sustainable transport [
1
–
6
]. However, the future is uncertain and
technological developments are advancing rapidly, so that mobility conditions linked to
AVs may be different from those expected, which would have spatial effects of a different
sign and magnitude that must be considered by urban and transport planners [7,8].
The immediate and most direct impacts will arise in the transport system itself. Due to
the new communication systems between vehicles (V2V), road infrastructures, and traffic
control centers, an increase in road capacity is expected, coupled with an improvement in
congestion levels [
9
,
10
]. However, by reducing trip costs and providing inclusive mobil-
ity to non-driving people, AVs could also lead to an increase in trip generations
[11–14]
,
either by capturing demand from public transport and other active modes or by induc-
ing demand for new trips. Soteropoulos et al. [
6
] pointed out that AVs could increase
trip distances and reduce the market share of public transport and active modes. For
Krueger et al. [
15
], they could provide a flexible solution compared to other modes to
complete the “last mile”, encouraging multimodality. Moreover, because AVs will free
users from driving, a significant reduction (of around 25–75%, depending on the various
Sustainability 2021,13, 1608. https://doi.org/10.3390/su13041608 https://www.mdpi.com/journal/sustainability

Sustainability 2021,13, 1608 2 of 16
authors) in the value of trip time perceived by users is generally expected [
16
–
18
], although
this reduction cannot be generalized to all cases [19].
If all these forecasts/assumptions become real, they may imply important changes
in accessibility. Childress et al. [
17
] stated that the decrease in the value of time and the
increase in the capacity of roads would lead to an improvement in accessibility in all
areas and especially in rural ones, which could imply an increase of up to 20% in the total
distance traveled by vehicles. Milakis et al. [
20
] pointed out that AVs may offer their users
an increased variety of accessible activities but will have conflicting social and spatial
implications. Moreover, Papa and Ferreira [
21
] identified critical governance decisions that
could affect accessibility levels.
The expected fast development of AVs can have a disruptive impact on transport
and lead to substantial changes in urban form and land use. Changes in accessibility can
modify the patterns of residential location and land use distribution, which would affect
the magnitude and direction of urban sprawl processes. Zakharenko [
22
] estimated that
urban areas could expand up to 7% towards the periphery. For Litman [
23
], the effects will
depend on transport and land use policies, with current policies implying an increase in
sprawl between 10% and 30%.
The main aim of this article is to evaluate the potential effects of different scenarios,
such as increases in infrastructure capacity or increases in demand caused by AVs, with
special attention to their effects on the location of population and activities, using land
use–transport interaction models (LUTI models). This type of evaluation on medium- to
long-term effects is an area of research, which is scarce in the academic literature despite
the potential use of LUTI models to support decision making [
6
]. We applied a LUTI model,
called TRANSPACE, to simulate a series of scenarios reflecting the range of foreseeable
alternatives in a specific urban system. The proposed model combines random utility
theory with hedonic regression techniques and a supply–demand equilibrium transport
model to estimate the location of population, economic activities, and transport patterns
in the zones of an urban system. This model was applied to the urban area of Santander
(Spain) as an example of the effects that autonomous driving could have in a medium-size
urban system.
Following these lines, the document is organized in the next sections. The subsequent
section provides a literature revision on the possible impacts that the introduction of AVs
may have on the location of urban agents. Then, the model used here to carry out the
simulations is presented. The case study is then introduced and the results of four scenarios
that reflect alternative future situations in terms of infrastructure capacity, and individual
or shared use of AVs are considered. Finally, the last section draws the main conclusions,
discusses the limitations of the model and identifies future research needs.
2. Autonomous Vehicles and Location of Population and Activities
The literature on the possible impacts that AVs can have on the location choices of
urban agents is still scarce, although it is growing fast. Earlier studies already showed that
AVs can generate increases in accessibility and in the trip distances traveled, which would
reinforce the processes of urban sprawl by making residence in peripheral and rural areas
more attractive [
6
,
17
,
24
]. These effects would occur due to the combined action of several
phenomena, but especially as a result of the reduction in trip time derived from the increase
in infrastructure capacity provided by the capabilities of AVs and by the reduction in the
value of trip time derived from the fact that traveling in an AV is less stressful and does not
require the driver’s attention.
These effects were evaluated by Meyer et al. [
25
], although without considering the
possible medium-term changes in the location of households resulting from the introduc-
tion of AVs. The authors considered possible increases in infrastructure capacity generated
by AVs (and the subsequent increase in specific location accessibility) and the rise in de-
mand derived from new users and empty trips. Furthermore, in the latter scenario, the
authors also took into account that some users could stop using public transport in favor

Sustainability 2021,13, 1608 3 of 16
of individual autonomous transport and that this induced demand could be generated
by accessibility improvements. All these scenarios indicated that accessibility improve-
ments would occur mainly in peripheral urban areas and nearby rural areas which could
significantly increase sprawl and make public transport competitive only in urban centers.
Subsequent research has also incorporated the possible effects on residential loca-
tions that AVs may involve, i.e., the fact that in the medium-term households may re-
locate given the aforementioned new accessibility conditions. In this sense, Zhang and
Guhathakurta [
26
] used an agent-based model combined with a residential location model
to study the impacts of a fleet of shared autonomous vehicles (SAVs) without considering
ridesharing. The residential choice model was based on discrete choice techniques consider-
ing the socioeconomic characteristics of the households, the characteristics of the properties,
the built environment, and the transport costs to work of each dwelling, including those
derived from commuting time and from ownership and use of the vehicle. The simulation
of the SAVs was carried out using an agent-based model in which 10,000 vehicles were
generated at the beginning of the simulation, and new vehicles were added if the waiting
times of any customer were longer than 15 min. The results of the modeling showed
that waiting times for SAVs were significantly higher in the periphery of the study area
(Atlanta Metropolitan Region, GA, USA), although the commuting costs were considerably
reduced. In addition, households with older members tended to be located closer to the
central business district (CBD) to achieve shorter waiting times for SAVs, while younger
households generally chose to locate further away to enjoy better housing or environment.
It was also found that, on average and in all types of households, commuting distances
increased with the availability of SAVs.
Other studies have gone further into the estimation of more precise residential choice
models to determine how the value of time may vary with the existence of AVs. This is
the case of a study by Krueger et al. [
27
] using a stated preference (SP) survey and mixed
logit discrete choice models. However, these authors did not find substantial changes in
the value of trip time and, therefore, in the capacity of AVs to change residential location
patterns. In a similar vein, using data collected with an SP survey, Carrese et al. [
28
]
estimated a modal choice model that included the use of private vehicles, carsharing, or
ridesharing and a binomial residential choice model between no change of location and
changing residential location to the suburbs of the study area (Rome, Italy). These models
combined with a transport simulation model allowed to simulate the consequences of
various scenarios with different ridesharing penetration. In this way, it was detected
that some households could relocate to more peripheral urban areas, which in the model
resulted in an increase in congestion and trip times, as opposed to central areas where the
effect was the opposite. Furthermore, these negative effects could only be avoided in the
scenario where the level of ridesharing penetration was 100%.
Finally, other authors have chosen to update pre-existing LUTI models to assess
scenarios with the presence of AVs, which may lead to more realistic modeling. This is
the case of Emberger and Pfaffenbichler [
29
], who used a modification of the Metropolitan
Activity Relocation Simulator (MARS) model, based on system dynamics, to quantify the
impacts of AVs on transport and land use in Austria. The model was adapted to consider
the fact that AVs could have an impact on the capacity of infrastructures, the location of
car parks, the value of time, or the possibility that new types of users (young, elderly,
and disabled) could access private or shared vehicles. In general, the scenarios proposed
implied an increase in vehicle-kilometers traveled by AVs of around 30%, taking into
account changes in the four factors mentioned above. Spatially, these changes mainly
affected the capital (Vienna), the peripheral areas of the cities, and their nearby rural areas.
Therefore, according to these simulations, an increase in the use of AVs could imply an
increase in private vehicle mobility and trip distances.
A similar line of research has been developed by Basu and Ferreira [
30
], in this case
using the LUTI model SimMobility for the evaluation of AV individual use and ridesharing
mode scenarios in a study area including a planning district of Singapore with a low market

Sustainability 2021,13, 1608 4 of 16
share for private vehicles. This agent-based model allows simulating urban agent choices
in the short (e.g., route choice), medium (e.g., activity choice), and long (e.g., residential
location choice) terms. By simulating various scenarios considering changes in the resi-
dential market, the authors detected that the presence of AVs in the district could further
reduce the presence of private vehicles in the area and induce gentrification effects, i.e., the
substitution of the existing population by another with a higher income level.
These studies indicate that knowledge about the impacts of AVs on urban systems is
still highly uncertain. In this sense, the use of LUTI models is advisable to simulate the
equilibrium between transport and land use subsystems in such a way that the effects of
AVs on accessibility and network flows are endogenized in the location models. In addition,
it is also interesting to consider the sites of economic activities, as these can be influenced
and in turn affect the location of the population, playing an important role in urban sprawl
processes.
3. Model Applied to the Simulation of Autonomous Vehicles Impacts
The simulation of scenarios was carried out using the TRANSPACE model, a tool
based on previous research [
31
,
32
]. TRANSPACE is a LUTI model that combines economic
base theory with discrete choice modeling to simulate changes in the location of both
households and local demand-oriented businesses and services. In addition, the model is
capable of estimating property prices using hedonic regression techniques to improve the
results of the residential location model (Figure 1). Transport simulation is carried out by
means of the Visum model (PTV AG, Karlsruhe, Germany) [
33
], which allows considering
the three traditional steps of travel demand and its network equilibrium assignment.
Sustainability 2021, 13, x FOR PEER REVIEW 4 of 17
A similar line of research has been developed by Basu and Ferreira [30], in this case
using the LUTI model SimMobility for the evaluation of AV individual use and rideshar-
ing mode scenarios in a study area including a planning district of Singapore with a low
market share for private vehicles. This agent-based model allows simulating urban agent
choices in the short (e.g., route choice), medium (e.g., activity choice), and long (e.g., resi-
dential location choice) terms. By simulating various scenarios considering changes in the
residential market, the authors detected that the presence of AVs in the district could fur-
ther reduce the presence of private vehicles in the area and induce gentrification effects,
i.e., the substitution of the existing population by another with a higher income level.
These studies indicate that knowledge about the impacts of AVs on urban systems is
still highly uncertain. In this sense, the use of LUTI models is advisable to simulate the
equilibrium between transport and land use subsystems in such a way that the effects of
AVs on accessibility and network flows are endogenized in the location models. In addi-
tion, it is also interesting to consider the sites of economic activities, as these can be influ-
enced and in turn affect the location of the population, playing an important role in urban
sprawl processes.
3. Model Applied to the Simulation of Autonomous Vehicles Impacts
The simulation of scenarios was carried out using the TRANSPACE model, a tool
based on previous research [31,32]. TRANSPACE is a LUTI model that combines eco-
nomic base theory with discrete choice modeling to simulate changes in the location of
both households and local demand-oriented businesses and services. In addition, the
model is capable of estimating property prices using hedonic regression techniques to im-
prove the results of the residential location model (Figure 1). Transport simulation is car-
ried out by means of the Visum model (PTV AG, Karlsruhe, Germany) [33], which allows
considering the three traditional steps of travel demand and its network equilibrium as-
signment.
Figure 1. Structure of the land use–transport interaction (LUTI) model considering the presence of
autonomous vehicles (AVs).
The relationship between land use models and the transport model is established
through accessibility indicators and trip costs in the network arcs. The accessibility indi-
cators considered are of the gravitational type [34] with two specifications, active and pas-
sive accessibility, respectively, which are
Figure 1.
Structure of the land use–transport interaction (LUTI) model considering the presence of
autonomous vehicles (AVs).
The relationship between land use models and the transport model is established
through accessibility indicators and trip costs in the network arcs. The accessibility indica-
tors considered are of the gravitational type [
34
] with two specifications, active and passive
accessibility, respectively, which are
Acc(o) = ∑
iexp[α2·Cost(o,di)] ·jobs(di)α1(1)
Acc(d) = ∑
inexp[β2·Cost(oi,d)] ·res(oi)β1o, (2)
where Cost is a measure of the trip cost between two zones, jobs(d
i
) are the jobs present in
zone d
i
as a measure of attraction and opportunities (active accessibility), and res(o
i
) are the

Sustainability 2021,13, 1608 5 of 16
residents present in zone o
i
who can access zone d(passive accessibility). Finally,
α1
and
β1
are the parameters corresponding to the attraction variables, while
α2
and
β2
are the
parameters capturing the power of trip costs. All these parameters can be estimated by
linearizing the expressions using logarithms and considering the production of trips in
a zone as a proxy of active accessibility and the attraction of trips in each zone as that of
passive accessibility. The estimated parameters can also be interpreted as accessibility’s
elasticity to percentual changes in the trip cost and opportunities specified in the indicators.
The model is based on the following simplifications to facilitate the modeling process:
1.
The modeled area is considered closed to avoid taking into account immigration/
emigration flows. This reduces the realism of the model and simplifies it by avoiding
modeling a phenomenon that is not considered fundamental to simulate the internal
dynamics of the area and the impacts of AVs;
2.
The model does not incorporate a sub-model of real estate supply, although it restricts
the possibilities of the analyzed areas to accommodate population and activities based
on their potential future growth;
3.
The location of the activities considered as belonging to the basic sector is not modeled
but taken as fixed and independent of accessibility to the population.
The location of the population is estimated by means of a logit-type model based on
the theory of random utility in which the head of the household/main worker chooses
the location that maximizes its utility. Because it is not possible for the modeler to know
exactly the utilities and, therefore, the ordering of alternatives that will be made by the
agents, the choice can be modeled in a probabilistic way, using an expression of the type
Pi
res−co nd (o|d) = exp[Vi(o
d)]
∑oexp[Vi(o
d)] , (3)
where
Pi
res−co nd (o

d)
is the probability that the head of the household/main worker of
type i(socioeconomic class) will choose to live in zone ogiven that he/she works in area
d. This choice probability is modeled through the systematic utility of choosing each of
the ozones conditioned to work in d
Vi(o
d)
. This systematic utility can include attributes
related to the structural characteristics of dwellings in the area, environmental features,
and conditions of accessibility and transport.
By means of these choice probabilities, it is possible to locate the number of workers w
of type ithat are located in each zone oas
wi(o) = ∑
d
Pi
res−co nd (o|d)·Em pi(d), (4)
where Emp
i
(d) is the total number of type iworkers present in each zone d. This implies
that the modeler must know, as exogenous data, how workers are disaggregated by socio-
economic class in each zone.
Finally, having estimated the number of workers in each area, it is possible to calculate
the total number of residents through the following expression:
res(o) = k(o)·∑
i
∑
d
Pi
res−co nd (o|d)·Em pi(d). (5)
This estimate is consistent with first simplification 1 and with economic base theory
whereby an increase in employment implies an increase in population. The parameter k(o)
is exogenous and can be estimated for each of the ozones in a differentiated way or for the
whole of the study area if more data are not available.

Sustainability 2021,13, 1608 6 of 16
The location of jobs belonging to the non-base sector is also calculated using a multi-
nomial logit model similar to (3) of the type
Pa(d) = exp[Va(d)]
∑dexp[Va(d)] , (6)
where the probability for a job belonging to the economic sector ato be located in dis given
by the systematic utility of a zone dcompared to the rest of the zones in the study area.
This systematic utility can be made dependent on different advantages of each location,
including its passive accessibility to the population because it applies only to non-basic
sectors oriented to domestic demand. The location of jobs in an economic sector ain each
zone is therefore given by
Empa(d) = Pa(d)·EMPa, (7)
where EMP
a
is the number of jobs in the economic sector apresent in the study area.
Given that the location of the population depends on the job location and that the location
of activities oriented to domestic demand depends on the location of the population, the
model takes into account that the solution is given by an equilibrium problem that can be
treated as a fixed-point problem [31,35].
The simulation of property prices is carried out using a hedonic regression model
where the prices of a property jlocated in a zone odepend on a series of characteristics,
which may correspond to structural characteristics of dwellings, those of the environment,
or conditions of accessibility and transport.
Finally, the transport model is based on a trip generation based on the data provided
by location models, a gravitational trip distribution model constraint to both origins (trip
production) and destinations (trip attractions), and a modal choice model that considers
the possibility of choosing between an individual AV and public transport. This model is
of the logit type and takes into account the total estimated trip times of each mode. The
model takes the form
PSAVij =e−0.043·TTSAVij
e−0.043·TTSAVij +e−0.632−0.043·TTPTij
∀i,j(8)
where P
SAVij
is the probability of choosing the SAV mode between zones ij,TT
SAVij
is the
total trip time in SAV between zones ij, and TT
PTij
is the total trip time between zones ij by
public transport.
The transport model considers demand and infrastructure capacity at peak times,
i.e., the time of the day with potentially more mobility problems. The LUTI model as
a whole was originally calibrated with data from 2008. The data used to calibrate the
model were obtained from four main sources: the Spanish Institute of Statistics (population
and housing census and annual registers of the population), the Institute of Regional
Statistics (Regional Company Directory), real estate portals and an Origin-Destination
survey carried out in the study area with data from a sample of households and their trips.
More information on the parameters estimated in the different models and indicators of
accessibility can be found in Coppola et al. [31].
4. Case Study and Results
4.1. Case Study
The model described in the previous section was applied to the urban area of the
Bay of Santander (Cantabria, Spain). This area has been defined as comprising nine
municipalities—Santander, Santa Cruz de Bezana, Astillero, Camargo, Piélagos, Medio
Cudeyo, Marina de Cudeyo, Villaescusa, and Ribamontán al Mar. These municipalities
have a total population of 280,581 inhabitants (data for 2019). Figure 2shows the popula-
tion density of the study area, which is concentrated in the city of Santander and in the
northwest-southeast corridor from Bezana to El Astillero. The population density in the

Sustainability 2021,13, 1608 7 of 16
most urbanized areas, and especially in the city of Santander, is quite high with values
of over 20,000 inhabitants per square kilometer. In order to facilitate the presentation
of results, five large zones have been defined within the study area—the urban center
of Santander (zone 1), which concentrates a high number of jobs; the highly inhabited
residential neighborhoods surrounding the urban center, which have a significant popu-
lation density (zone 2); the periphery of the municipality of Santander (zone 3), with a
peri-urban character; the area of influence closest to the central city (zone 4), which still has
high population densities and several population centers dependent on Santander such as
Muriedas, Maliaño, and El Astillero; and the area furthest away (zone 5) with centers of a
more rural nature (see Figure 2).
Sustainability 2021, 13, x FOR PEER REVIEW 7 of 17
4. Case Study and Results
4.1. Case Study
The model described in the previous section was applied to the urban area of the Bay
of Santander (Cantabria, Spain). This area has been defined as comprising nine municipa-
lities—Santander, Santa Cruz de Bezana, Astillero, Camargo, Piélagos, Medio Cudeyo,
Marina de Cudeyo, Villaescusa, and Ribamontán al Mar. These municipalities have a total
population of 280,581 inhabitants (data for 2019). Figure 2 shows the population density
of the study area, which is concentrated in the city of Santander and in the northwest-
southeast corridor from Bezana to El Astillero. The population density in the most urban-
ized areas, and especially in the city of Santander, is quite high with values of over 20,000
inhabitants per square kilometer. In order to facilitate the presentation of results, five large
zones have been defined within the study area—the urban center of Santander (zone 1),
which concentrates a high number of jobs; the highly inhabited residential neighborhoods
surrounding the urban center, which have a significant population density (zone 2); the
periphery of the municipality of Santander (zone 3), with a peri-urban character; the area
of influence closest to the central city (zone 4), which still has high population densities
and several population centers dependent on Santander such as Muriedas, Maliaño, and
El Astillero; and the area furthest away (zone 5) with centers of a more rural nature (see
Figure 2).
Figure 2. Population density in the study area (2019) (left) and division of the study area into large zones (right).
4.2. Scenarios and Results
Given that the implementation of AVs still presents many uncertainties, a methodol-
ogy based on scenario building has been chosen to propose different situations that could
arise in the future. Four large scenarios have been defined as representative of the impli-
cations of AVs to simulate their effects on the transport system, the location of population
and activities, and accessibility conditions, which are as follows:
1. Increase in the capacity of interurban infrastructures. This scenario simulates the ef-
fects of the increase in capacity that could take place with a more efficient automated
driving of AVs, which, as private vehicles in the initial stages, would only be availa-
ble in interurban areas;
2. Increase in the capacity of interurban and urban infrastructures. In this more ad-
vanced scenario, AVs can be used privately, both in interurban areas and inside cities,
given the improvements in their technological capabilities;
3. Increase in the capacity of interurban and urban infrastructure + induced demand.
Unlike scenario 2, this one considers that the improvement in accessibility of specific
areas can generate new trips due to the reduction in trip costs and a larger number
of nearby employment opportunities;
Figure 2. Population density in the study area (2019) (left) and division of the study area into large zones (right).
4.2. Scenarios and Results
Given that the implementation of AVs still presents many uncertainties, a methodology
based on scenario building has been chosen to propose different situations that could arise
in the future. Four large scenarios have been defined as representative of the implications
of AVs to simulate their effects on the transport system, the location of population and
activities, and accessibility conditions, which are as follows:
1.
Increase in the capacity of interurban infrastructures. This scenario simulates the
effects of the increase in capacity that could take place with a more efficient automated
driving of AVs, which, as private vehicles in the initial stages, would only be available
in interurban areas;
2.
Increase in the capacity of interurban and urban infrastructures. In this more ad-
vanced scenario, AVs can be used privately, both in interurban areas and inside cities,
given the improvements in their technological capabilities;
3.
Increase in the capacity of interurban and urban infrastructure + induced demand.
Unlike scenario 2, this one considers that the improvement in accessibility of specific
areas can generate new trips due to the reduction in trip costs and a larger number of
nearby employment opportunities;
4.
Increase in urban and intra-urban infrastructure capacity + induced demand + in-
crease in users and empty trips (SAVs). In this scenario, in addition to what has
been examined in the previous ones, AVs can be sequentially shared (carsharing),
which could attract new users from other modes such as public transport or on foot,
and producing new empty trips.
In all four scenarios, the TRANSPACE model was used to simulate the effects of
increased infrastructure capacity and the existence of SAVs on accessibility and trip times
and, therefore, on the location of population and activities in the urban area studied.
Simulated vehicles will be considered to be fully autonomous, i.e., level 5 according

Sustainability 2021,13, 1608 8 of 16
to the Society of Automotive Engineers (SAE) On-Road Automated Vehicle Standards
Committee [36] classification.
4.2.1. Scenario 1
This first scenario simulates a situation in which AVs already present a notable tech-
nological development but are not yet suitable for circulation in urban areas given their
greater complexity regarding coexistence with other motorized modes and with pedestri-
ans/cyclists, and the presence of complex intersections and other factors. Despite this, the
capacity of AVs to react more quickly to other vehicle movements and their communication
by means of V2V protocols may imply that their use of existing infrastructures is more
efficient than at present.
The capacity changes applied to interurban roads are those calculated by
Shladover et al. [
10
] and Friedrich [
37
] for motorways. These authors estimated that
capacity increases with cooperative AVs can be estimated around 80% if AVs market pen-
etration rates are close to 100%. These results are also in line with those simulated by
Liu et al. [38].
The increase in capacity of interurban infrastructures implies improved trip times,
especially in areas closest to motorways, with only certain areas of access to the central city
experiencing a slight increase in trip times due to more congestion (Table 1and Figure 3).
These same intermediate areas, which are close to the nucleus of Santander but are not
part of it (zone 4), are the ones that benefit most from the increases in active and passive
accessibility (Figure 3), while the areas further away from the urban nucleus of Santander or
the central areas of the city present, in some cases, reductions in their levels of accessibility
to jobs and population. In terms of the distribution of the latter, the model shows how zone
4 could capture more population while zones closer to the urban nucleus (zones 2 and 3)
could reduce it. This can also be seen in the increase in kilometers traveled outside the
central city. On the other hand, there would be no significant change in the number of jobs.
Therefore, these results point to a moderate process of population sprawl in the study area
that is accompanied by improvements in mobility and accessibility.
4.2.2. Scenario 2
In this case, in addition to the changes already simulated under scenario 1, there is a
40% increase in the capacity of urban roads according to estimates made by Friedrich [
37
].
This may allow these areas to also benefit from improvements in trip times and reduced
congestion and therefore increase their accessibility and attractiveness as places to live. The
simulation carried out shows how, on this occasion, the central city does not lose population
but even gains a small number of inhabitants in the residential neighborhoods (zone 2)
(Figure 4) and jobs in the city center (zone 1) (Table 1). In this case, the improvement in trip
times benefits both the city and its area of influence similarly. This also results in a notable
increase in accessibility in almost all zones except for some of the most peripheral ones
(zone 5). This scenario can therefore be considered optimistic with respect to the effects of
AVs, where the increases in capacity benefit the whole area and especially the previously
more congested central urban area, thus avoiding the urban sprawl effects observed in
scenario 1. Furthermore, the reduction in traffic congestion also allows for a reduction in
vehicle-kilometers traveled and measured trip distances, thus reducing pollutant emissions
and/or energy costs derived from car mobility.

Sustainability 2021,13, 1608 9 of 16
Sustainability 2021, 13, x FOR PEER REVIEW 9 of 17
Figure 3. Changes in the distribution of the population (top-left), average trip times from one zone to the others (top-right),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 1.
4.2.2. Scenario 2
In this case, in addition to the changes already simulated under scenario 1, there is a
40% increase in the capacity of urban roads according to estimates made by Friedrich [37].
This may allow these areas to also benefit from improvements in trip times and reduced
congestion and therefore increase their accessibility and attractiveness as places to live.
The simulation carried out shows how, on this occasion, the central city does not lose
population but even gains a small number of inhabitants in the residential neighborhoods
(zone 2) (Figure 4) and jobs in the city center (zone 1) (Table 1). In this case, the improve-
ment in trip times benefits both the city and its area of influence similarly. This also results
in a notable increase in accessibility in almost all zones except for some of the most pe-
ripheral ones (zone 5). This scenario can therefore be considered optimistic with respect
to the effects of AVs, where the increases in capacity benefit the whole area and especially
the previously more congested central urban area, thus avoiding the urban sprawl effects
observed in scenario 1. Furthermore, the reduction in traffic congestion also allows for a
reduction in vehicle-kilometers traveled and measured trip distances, thus reducing pol-
lutant emissions and/or energy costs derived from car mobility.
Figure 3.
Changes in the distribution of the population (
top-left
), average trip times from one zone to the others (
top-right
),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 1.
Sustainability 2021, 13, x FOR PEER REVIEW 10 of 17
Figure 4. Changes in the distribution of the population (top-left), average trip times from one zone to the others (top-right),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 2.
4.2.3. Scenario 3
In this scenario, trip generation in the LUTI model is modified to take into account
the effect of induced demand, which can be derived from the improvements in active ac-
cessibility estimated under scenario 2. The relative increase in accessibility is calculated as
, 2, ,
( / ) 1
ACC i E i BASE i
AA  
,
(9)
where AE2,i is the accessibility in zone i calculated for scenario 2 and ABASE,i is the accessi-
bility in zone i calculated for the base scenario.
To obtain trip production considering the induced demand, the combined elasticity
of cost and estimated opportunities for the active accessibility indicator ε = 0.385 was ap-
plied [31]. Thus, the new trip production
'
i
P
was calculated as
',
(1 )
i i ACC i
P P i

    
(10)
The model then balances the new estimated trip production with trip attraction and
generates the total trip distribution matrix using the double-constrained gravitational
model used in the TRANSPACE model.
In this case, the population of the central city grows even more than under scenario
2 due to the increase experienced by zone 2 (Figure 5 and Table 1), and jobs remain prac-
tically stable despite the additional demand induced by the improvements in accessibility.
This is because, within the central city, trip times continue to show some improvement
Figure 4.
Changes in the distribution of the population (
top-left
), average trip times from one zone to the others (
top-right
),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 2.

Sustainability 2021,13, 1608 10 of 16
4.2.3. Scenario 3
In this scenario, trip generation in the LUTI model is modified to take into account
the effect of induced demand, which can be derived from the improvements in active
accessibility estimated under scenario 2. The relative increase in accessibility is calculated as
∆ACC,i= ( AE2,i/AB ASE,i)−1, (9)
where A
E2,i
is the accessibility in zone icalculated for scenario 2 and A
BASE,i
is the accessi-
bility in zone icalculated for the base scenario.
To obtain trip production considering the induced demand, the combined elasticity
of cost and estimated opportunities for the active accessibility indicator
ε
= 0.385 was
applied [31]. Thus, the new trip production P0
iwas calculated as
P0
i=Pi·(1+ε∆ACC,i)∀i(10)
The model then balances the new estimated trip production with trip attraction and
generates the total trip distribution matrix using the double-constrained gravitational
model used in the TRANSPACE model.
In this case, the population of the central city grows even more than under scenario 2
due to the increase experienced by zone 2 (Figure 5and Table 1), and jobs remain practically
stable despite the additional demand induced by the improvements in accessibility. This
is because, within the central city, trip times continue to show some improvement (zones
1 and 2) except in the access areas (zone 3). However, induced demand makes increases
in active accessibility already scarce in this scenario with the exception of zone 1, while
passive accessibility even decreases in zone 4 due to population loss. On the other hand,
the number of kilometers traveled shows a strong increase due to new induced AV trips,
although the average distance traveled is lower than in the base scenario. This indicates
that even considering a greater trip generation, there does not seem to be a process of
urban sprawl derived from the increases in capacity estimated for scenario 2, but there is
an increase in the number of journeys made by AVs even though the modal split does not
change because public transport users also increase in number.
Sustainability 2021, 13, x FOR PEER REVIEW 11 of 17
(zones 1 and 2) except in the access areas (zone 3). However, induced demand makes in-
creases in active accessibility already scarce in this scenario with the exception of zone 1,
while passive accessibility even decreases in zone 4 due to population loss. On the other
hand, the number of kilometers traveled shows a strong increase due to new induced AV
trips, although the average distance traveled is lower than in the base scenario. This indi-
cates that even considering a greater trip generation, there does not seem to be a process
of urban sprawl derived from the increases in capacity estimated for scenario 2, but there
is an increase in the number of journeys made by AVs even though the modal split does
not change because public transport users also increase in number.
Figure 5. Changes in the distribution of the population (top-left), average trip times from one zone to the others (top-right),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 3.
4.2.4. Scenario 4
In this last scenario, in addition to increases in the capacity of urban and interurban
infrastructures and induced demand, AVs can be used sequentially (carsharing) so that
new users, who did not have access to private AVs, will have them available and there
will also be empty trips derived from services between different users.
The methodology applied to calculate this scenario is similar to that used by Meyer
et al. [25], which is based on updating the car–trip matrix by considering a fixed increase
derived from the transfer of trip distances made by minors and those over 64 to car mode.
However, in this case, only the increase in trips derived from active modes (walking and
cycling) is considered, while the simulation of the possible transfer of users from public
transport is calculated using the logit model presented in Section 3.
Figure 5.
Changes in the distribution of the population (
top-left
), average trip times from one zone to the others (
top-right
),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 3.

Sustainability 2021,13, 1608 11 of 16
4.2.4. Scenario 4
In this last scenario, in addition to increases in the capacity of urban and interurban
infrastructures and induced demand, AVs can be used sequentially (carsharing) so that
new users, who did not have access to private AVs, will have them available and there will
also be empty trips derived from services between different users.
The methodology applied to calculate this scenario is similar to that used by
Meyer et al. [25]
, which is based on updating the car–trip matrix by considering a fixed
increase derived from the transfer of trip distances made by minors and those over 64
to car mode. However, in this case, only the increase in trips derived from active modes
(walking and cycling) is considered, while the simulation of the possible transfer of users
from public transport is calculated using the logit model presented in Section 3.
Based on data from a household survey carried out in 2015 in the city of Santander,
the total distance traveled by minors and those over 64 years old using active modes was
estimated in 19% of the total distance traveled by all car users. In this way, the updated
demand between each pair of zones D0
ij is equal to
D0
ij =Di j ·γ∀i,j, (11)
where
γ
is equal to 1.19 under the assumption that all trips made by these users involving
active modes will be made in SAV. This assumption, although strong, means only a 19%
increase in trips, which is considered realistic.
In addition, the existence of empty journeys resulting from pick-up trips between
users or parking of the vehicle has also been considered. It is assumed that, on average,
each person can generate one empty trip which, knowing that each individual performs
an average of 2.81 trips in the study area, results in a 36% increase in total trips caused by
empty ones, i.e.,
D00
ij =D0
ij ·(1+1/2.81)∀i,j. (12)
Both in terms of capturing trips from minors and users over 64 years old, and in terms
of generating empty trips, it is assumed that their distribution will be equal to that of the
demand given by the trip matrix in the previous scenario. This assumption is considered
reasonable given that it affects groups that are found all over the study area and empty
trips can be distributed equally among all trips made.
These hypotheses lead to the results shown in Figure 6and Table 1. The increase in
demand for transport in SAVs leads to an increase in average journey times in all areas,
although especially outside the central city, and a fall in active and passive accessibility.
This means that the distribution of the population is not greatly affected compared to the
current situation and that in any case, the residential areas of the central city (zone 2) may
grow slightly compared to a reduction in the more peripheral zones of the study area
(zone 5) as was already the case in scenario 3. On the other hand, there is a slight increase
in jobs outside the city, although mainly near the main communication highways (zones 3
and 4). Therefore, in this scenario, AVs would not favor urban sprawl or only to a limited
extent, although the effects of increased infrastructure capacity would be neutralized
in terms of increased accessibility and shorter journey times due to the higher demand
for mobility, affecting the periphery more than the central city. Furthermore, the total
distances and the average distance traveled by vehicles would increase considerably, which
could have negative repercussions in terms of polluting emissions or energy consumption
(see Figure 7).

Sustainability 2021,13, 1608 12 of 16
Table 1. Comparison between indicators under the simulated scenarios and the base scenario.
Scenario/Zone
Change in the
Choice of Car
Mode
Change in Average
Trip Distance
Covered in AV
Kilometers
Covered
by AV
Trip
Time
Active Ac-
cessibility
Passive Ac-
cessibility Population Employment
Scenario 1
Z1
0.1% −0.3%
0.9% −4.3% −0.4% 5.9% 0.0% 0.2%
Z2
−1.9% −3.2% 1.6% 1.2% −0.7% 0.0%
Z3
−0.7% −0.8% 4.4% −1.0% −1.0% −0.1%
Z4
1.1% −
13.3%
30.6% 28.4% 2.1% −0.1%
Z5
0.3% −5.9% 5.7% 6.6% 0.0% 0.0%
Scenario 2
Z1
0.8% −2.9%
1.2% −
20.9%
94.6% 187.1% 0.0% 1.6%
Z2
−1.8% −
21.1%
48.4% 43.0% 0.4% 0.0%
Z3
−3.3% −
10.4%
37.0% 37.6% −1.3% −0.7%
Z4
−1.8% −
21.1%
33.4% 29.7% 1.0% −0.8%
Z5
−2.1% −
13.2%
6.6% 7.2% −1.9% −0.2%
Scenario 3
Z1
0.0% −1.4%
40.7% −4.1% 17.4% 43.9% 0.0% 0.2%
Z2
24.9% −3.8% 0.7% 1.7% 0.8% 0.0%
Z3
25.0% 4.6% −1.2% 11.6% 0.0% −0.1%
Z4
55.7% 0.3% 0.7% −2.0% −2.3% −0.1%
Z5
1.9% 1.2% 0.9% 0.7% 0.9% 0.0%
Scenario 4
Z1
2.1%/0.0% 15.0%
137.7% 30.6% −58.3% −71.1% 0.0% −0.8%
Z2
111.1% 37.2% −41.9% −31.8% 0.7% −0.3%
Z3
107.2% 48.3% −73.0% −77.8% 0.0% 0.6%
Z4
170.9% 41.2% −28.2% −34.4% 0.1% 0.7%
Z5
69.3% 49.9% −24.6% −27.6% −2.7% 0.1%
1Without considering empty trips.
Sustainability 2021, 13, x FOR PEER REVIEW 13 of 17
Figure 6. Changes in the distribution of the population (top-left), average trip times from one zone to the others (top-right),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 4.
Table 1. Comparison between indicators under the simulated scenarios and the base scenario.
Scenario/Zone
Change in the
Choice of Car
Mode
Change in Average
Trip Distance
Covered in AV
Kilometers
Covered by
AV
Trip
Time
Active
Accessibility
Passive
Accessibility
Population
Employment
Scenario 1
Z1
0.1%
−0.3%
0.9%
−4.3%
−0.4%
5.9%
0.0%
0.2%
Z2
−1.9%
−3.2%
1.6%
1.2%
−0.7%
0.0%
Z3
−0.7%
−0.8%
4.4%
−1.0%
−1.0%
−0.1%
Z4
1.1%
−13.3%
30.6%
28.4%
2.1%
−0.1%
Z5
0.3%
−5.9%
5.7%
6.6%
0.0%
0.0%
Scenario 2
Z1
0.8%
−2.9%
1.2%
−20.9%
94.6%
187.1%
0.0%
1.6%
Z2
−1.8%
−21.1%
48.4%
43.0%
0.4%
0.0%
Z3
−3.3%
−10.4%
37.0%
37.6%
−1.3%
−0.7%
Z4
−1.8%
−21.1%
33.4%
29.7%
1.0%
−0.8%
Z5
−2.1%
−13.2%
6.6%
7.2%
−1.9%
−0.2%
Scenario 3
Z1
0.0%
−1.4%
40.7%
−4.1%
17.4%
43.9%
0.0%
0.2%
Z2
24.9%
−3.8%
0.7%
1.7%
0.8%
0.0%
Z3
25.0%
4.6%
−1.2%
11.6%
0.0%
−0.1%
Z4
55.7%
0.3%
0.7%
−2.0%
−2.3%
−0.1%
Z5
1.9%
1.2%
0.9%
0.7%
0.9%
0.0%
Scenario 4
Z1
2.1%/0.0% 1
5.0%
137.7%
30.6%
−58.3%
−71.1%
0.0%
−0.8%
Z2
111.1%
37.2%
−41.9%
−31.8%
0.7%
−0.3%
Z3
107.2%
48.3%
−73.0%
−77.8%
0.0%
0.6%
Z4
170.9%
41.2%
−28.2%
−34.4%
0.1%
0.7%
Figure 6.
Changes in the distribution of the population (
top-left
), average trip times from one zone to the others (
top-right
),
active accessibility (bottom-left), and passive accessibility (bottom-right); scenario 4.

Sustainability 2021,13, 1608 13 of 16
Sustainability 2021, 13, x FOR PEER REVIEW 14 of 17
Z5
69.3%
49.9%
−24.6%
−27.6%
−2.7%
0.1%
1 Without considering empty trips.
Figure 7. Difference between the volume of vehicles assigned in scenario 4 and the base scenario.
5. Conclusions
The potential spatial repercussions of autonomous vehicles are still unknown, alt-
hough they may be very significant and lead to a profound reorganization of cities as we
know them. To study these implications is complex given the important interrelations be-
tween transport systems and land uses and due to all the uncertainties that the technolog-
ical and organizational development of AVs still present. In this research, a scenario build-
ing technique and a LUTI model were used to try to shed light on these possible effects,
and in this way, help adjust policy interventions within the framework of planning prac-
tice. As the applicability of the results depends on urban configuration characteristics, the
results of this study will be particularly relevant for countries and regions with compara-
ble urban structures, such as medium-sized European cities. In addition, the results ob-
tained show that AVs could have a significant impact depending on the urban context in
which they are implemented. Considering that AVs will generate an increase in the ca-
pacity of interurban infrastructures, augmented accessibility is expected, especially in mu-
nicipalities located in the surroundings of urban areas and near highways, favoring a
moderate sprawl of the population, which confirms previous findings [25]. If AVs allow
an increase in the capacity of existing infrastructures, both interurban and urban, the ef-
fects on population and activity location could be limited, and notable gains in trip times
and accessibility would be obtained which could benefit central cities more than peri-ur-
ban or rural areas. In addition, the reduced congestion could also mean a decrease in ve-
hicle kilometers traveled, although public transport could see its share reduced in the
modal split.
However, if these increases in infrastructure capacity, shorter trip times, and greater
accessibility result in a higher trip generation, either by private AV or by SAVs, some of
these benefits could be lost. The higher number of trips could significantly increase total
vehicle-kilometers traveled and average trip distances, which could have important neg-
ative externalities in terms of greater pollution and/or greater energy consumption, as
Figure 7. Difference between the volume of vehicles assigned in scenario 4 and the base scenario.
5. Conclusions
The potential spatial repercussions of autonomous vehicles are still unknown, al-
though they may be very significant and lead to a profound reorganization of cities as we
know them. To study these implications is complex given the important interrelations
between transport systems and land uses and due to all the uncertainties that the techno-
logical and organizational development of AVs still present. In this research, a scenario
building technique and a LUTI model were used to try to shed light on these possible
effects, and in this way, help adjust policy interventions within the framework of planning
practice. As the applicability of the results depends on urban configuration characteristics,
the results of this study will be particularly relevant for countries and regions with com-
parable urban structures, such as medium-sized European cities. In addition, the results
obtained show that AVs could have a significant impact depending on the urban context
in which they are implemented. Considering that AVs will generate an increase in the
capacity of interurban infrastructures, augmented accessibility is expected, especially in
municipalities located in the surroundings of urban areas and near highways, favoring a
moderate sprawl of the population, which confirms previous findings [
25
]. If AVs allow an
increase in the capacity of existing infrastructures, both interurban and urban, the effects on
population and activity location could be limited, and notable gains in trip times and acces-
sibility would be obtained which could benefit central cities more than peri-urban or rural
areas. In addition, the reduced congestion could also mean a decrease in vehicle kilometers
traveled, although public transport could see its share reduced in the modal split.
However, if these increases in infrastructure capacity, shorter trip times, and greater
accessibility result in a higher trip generation, either by private AV or by SAVs, some of these
benefits could be lost. The higher number of trips could significantly increase total vehicle-
kilometers traveled and average trip distances, which could have important negative
externalities in terms of greater pollution and/or greater energy consumption, as pointed
out by other authors [
6
,
17
,
26
,
29
,
39
]. In the last simulated scenario, both accessibility to
population and jobs and trip times could be significantly degraded in urban areas, which
could imply moderate sprawl effects, especially on jobs. However, mobility could be
hampered even more in the most peripheral areas, a phenomenon that was also detected

Sustainability 2021,13, 1608 14 of 16
by Carrese et al. [
28
] in Rome. This implies that if vehicles were autonomous, and even if
they were shared sequentially, their possible negative effects on mobility and accessibility
could be felt, especially by the share loss of other transport modes between segments of
users who previously had no access to or used cars to a lesser extent, and by the effect of
empty journeys between different services.
To consider different scenarios that reflect the range of foreseeable alternatives gives
decision makers the opportunity to anticipate by formulating policies and actions that
minimize or mitigate negative effects caused by AVs and at the same time promote their
potential benefits. Measures could include policies aimed at promoting active modes and
public transport, which could be enhanced given the possibilities offered by automation,
by restricting the use of AVs in certain urban areas or establishing appropriate pricing
for their use [
40
]. Dynamic road pricing to reduce kilometers traveled could easily be
implemented in AVs thanks to in-vehicle navigation and communication systems [
39
].
The automation of public transport could be particularly useful in counteracting the sprawl
effect of AVs, while also making it possible to reduce their associated environmental
impacts [6,29,41].
It should be noted that the simulations carried out have not taken into account the
fact that AVs could be shared on the same trip (ridesharing), which could reduce some
of the most negative effects detected. The promotion of autonomous mobility through
ridesharing could therefore be another of the policies which would ensure a future of
sustainable mobility compatible with the presence of AVs, and it will be examined in
subsequent studies.
Author Contributions:
Conceptualization, R.C., S.N., and J.L.M.; methodology, R.C. and E.G.-G.;
validation, R.C., S.N., and J.L.M.; formal analysis, R.C. and E.G.-G.; investigation, R.C., S.N., and E.G.-
G.; resources, S.N. and J.L.M.; data curation, R.C. and J.L.M.; writing—original draft preparation,
R.C. and E.G.-G.; writing—review and editing, S.N. and J.L.M.; visualization, R.C. and E.G.-G.;
supervision, S.N.; project administration, S.N. and J.L.M.; funding acquisition, S.N. and J.L.M.
All authors have read and agreed to the published version of the manuscript.
Funding:
This work is based on two research projects—“InnovAtive Urban and Transport plan-
ning tOols for the implemeNtation of new mObility systeMs based On aUtonomouS driving” –
AUTONOMOUS, 2020–2023, funded by the Spanish Ministry of Science and Innovation/ERDF
(EU)-State Programme for Knowledge Generation and Scientific and Technological Strengthening
of the R&D&i System (PID2019-110355RB-I00); and “Autonomous share mobility for tomorrow’s
liveable cities” – MOVI-CITY, 2019–2020, resulting from a Call of the University of Cantabria and
funded by the Department of Universities and Research, Environment, and Social Policy of the
Government of Cantabria (Spain).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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